Details

Autor(en) / Beteiligte

• Xue, Jie
• Yip, Tsz Leung
• Wu, Bing
• Wu, Chaozhong
• van Gelder, P.H.A.J.M.

Titel

A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China

Ist Teil von

• Renewable energy, 2021-07, Vol.172, p.897-917

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2021

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. •A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed.•A three-layer decision-making framework is established for the OWT selection.•Comprehensively consider each influencing factor’s qualitative and quantitative characteristics.•Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation.